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Comparing Speed-of-Sight studies using rendered vs. natural images
2010
Journal of Vision
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A critical parameter in biological vision is the amount of time required to recognize an object. This time scale yields information about the algorithm used by the brain to detect objects. ...
Viewpoint invariant object recognition is both an essential capability of biological vision and a key goal of computer vision systems. ...
target
mask
• Constrain neural network model (feedforwad vs. feedback) by measuring
time scale of human decision in 2 alternative forced-choice tests.
• ...
doi:10.1167/10.7.986
fatcat:nacgpip62jcflpl6uqj6c3x5ie
Distributed Architecture for Intelligent Robotic Assembly Part III: Design of the Invariant Object Recognition System
[chapter]
2006
Manufacturing the Future
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In this sense, the described technique for object recognition is accomplished using an Artificial Neural Network (ANN) architecture which receives a descriptive vector called CFD&POSE as the input. ...
Invariant Object Recognition Recognising an object using a vision system involves a lot of calculations and mathematical processes which have to be implemented and tested in order to be used in a real ...
doi:10.5772/5056
fatcat:u3dxua7rcjadlgunqferqdfvei
Optimal Structures of a Neural Network Based on OpenCV for a Golf Ball Recognition
골프공 인식을 위한 OpenCV 기반 신경망 최적화 구조
2015
The Journal of the Korea institute of electronic communication sciences
골프공 인식을 위한 OpenCV 기반 신경망 최적화 구조
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ABSTRACT In this paper the optimal structure of a neural network based on OpenCV for a golf ball recognition and the intensity of ROI(Region Of Interest) are calculated. ...
The system is composed of preprocess, image processing and machine learning, and a learning model is obtained by multi-layer perceptron using the inputs of 7 Hu's invariant moments, box ration extracted ...
In this paper, an optimal structure of a neural network based on OpenCV is proposed for a golf ball recognition. ...
doi:10.13067/jkiecs.2015.10.2.267
fatcat:54nw4t4bwjanffoafi5yqnanyy
Pattern Recognition in Digital Images using Fractals
2019
International Journal of Engineering and Advanced Technology
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This approach has for the first time allowed the pattern recognition problem to be solved in a way that is invariant of rotation and scale. ...
This research has stemmed in the design and implementation of a new algorithm for general pattern recognition based on the use of fractal image compression. ...
There are many techniques used for automatic pattern recognition [1] . Artificial Neural Networks (ANN) is the most widely used technique for pattern recognition. ...
doi:10.35940/ijeat.b4229.129219
fatcat:ku3q46bxebfr5bcwosggcmmjea
AIRCRAFT RECOGNITION SYSTEM USING A SIMPLE INVARIANT COMPLEX-NEURAL NETWORK
2001
International Conference on Aerospace Sciences and Aviation Technology
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This type of neural networks is commonly used as pattern classifiers, with complex moment invariant feature vector as input. ...
Complex Neural Network (1,4,51:
Complex Neural Network (CNN) Architecture: An CNN is a group of Artificial Neurons interconnected via an interconnection structure. ...
For aircraft recognition using complex moments are a recent one of the most useful features that can be extracted from an image because they can be invariant to translation, rotation, and scaling of the ...
doi:10.21608/asat.2001.31145
fatcat:ygzktqvchjekxne24pz4t4rzcq
Real Time Recognition of Handwritten Devnagari Signatures without Segmentation Using Artificial Neural Network
2013
International Journal of Image Graphics and Signal Processing
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This paper presents a real time or online method for recognition and verification handwritten signatures by using NN architecture. ...
For this purpose Neural Networks (NN) can be applied in the process of verification of handwritten signatures that are electronically captured. ...
An approach using Artificial Neural Network is considered for recognition of Handwritten Devnagari Signatures. In this paper a method is proposed for recognition of Devnagari handwritten signature. ...
doi:10.5815/ijigsp.2013.04.04
fatcat:annsqixiczda7ir3waq6bxq3n4
Research on Substation Real-Time Object Recognition Algorithm Based on Deep Learning
2018
DEStech Transactions on Engineering and Technology Research
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Our work is mainly the following: (1) Using the vgg deep convolutional network architecture as the infrastructure. (2) Real-time recognition of objects in video. (3) The system framework can be used in ...
The more advanced system based on the tensorflow framework is proposed for deep neural network recognition of objects in this paper. ...
This article uses a deep CNN framework for real-time recognition of video objects, which can be applied in many research areas. ...
doi:10.12783/dtetr/pmsms2018/24908
fatcat:36tpm3ddfrfcxiqkyqgximmjg4
Real-Time Object Recognition Using Deep-Learning
2021
Academic Journal of Nawroz University
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The great interest at the moment is focused on the field of technology, especially artificial intelligence, also do not devoid of our daily life of the use of phone applications and computer programs, ...
Finally, this approach has been created this project to save time and effort for the users instead of searching on a specific tool that they need about its name, how to use so we tried to facility this ...
They are also known as shift invariant or space invariant artificial neural networks (SIANN) [31], based on their shared-weights architecture and translation invariance characteristics. ...
doi:10.25007/ajnu.v10n2a1073
fatcat:xzprgi5ux5dztip3qfxtglqrh4
ANN and SVM Based War Scene Classification Using Invariant Moments and GLCM Features: A Comparative Study
2012
International Journal of Machine Learning and Computing
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The extracted features are trained and tested with (i) Artificial Neural Networks (ANN) using feed forward back propagation algorithm and (ii) Support Vector Machines (SVM) using radial basis kernel function ...
The complete work is experimented in Matlab 7.6.0 using real world dataset. ...
In this work we use feed-forward artificial neural network using backpropagation algorithm. ...
doi:10.7763/ijmlc.2012.v2.255
fatcat:cbmxsloddre27j5z52mtnbv2ya
An Analysis on Object Recognition Using Convolutional Neural Networks
2021
International Journal of Advanced Trends in Computer Science and Engineering
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Several significant techniques have been introducedfor image processing and object detection owing to this advancement. ...
The promising features and transfer learning of ConvolutionalNeural Network (CNN) havegained much attention around the globe by researchers as well as computer vision society, as a result of which, several ...
2) What are other artificial architectures neural network has been used so far for the object detection and recognition purposes? ...
doi:10.30534/ijatcse/2021/611032021
fatcat:5wl6d6no2ncyfb4ccifvf7zjsy
Face Recognition for Human Identification using BRISK Feature and Normal Distribution Model
2019
Zenodo
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Our proposed feature is applied with Artificial Neural Networks to recognize face for human identification. ...
Khin Mar Thi "Face Recognition for Human Identification using BRISK Feature and Normal Distribution Model" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN ...
Specially thank for allowance of free to use the extended Yale Face Database B for research purposes. ...
doi:10.5281/zenodo.3590557
fatcat:6oiqij7xgbanhh2xtixdbnpnt4
The ripple pond: enabling spiking networks to see
2013
Frontiers in Neuroscience
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(TP) suitable for recognition by temporal coding learning and memory networks. ...
Key aspects in the proposed approach include utilizing the spatial properties of physically embedded neural networks and propagating waves of activity therein for information processing, using dimensional ...
What minimal neural networks could possibly achieve the task of real-time view invariant recognition, which is so ubiquitous in animals, even those with miniscule nervous systems (Van der Velden et al ...
doi:10.3389/fnins.2013.00212
pmid:24298234
pmcid:PMC3829577
fatcat:ptjbjm27y5bubamykg43wtsw4q
The Ripple Pond: Enabling Spiking Networks to See
[article]
2013
arXiv
pre-print
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rapid, unsupervised, scale and rotation invariant object recognition using efficient spatio-temporal spike coding. ...
Key aspects in the proposed approach include utilising the spatial properties of physically embedded neural networks and propagating waves of activity therein for information processing, using dimensional ...
What minimal neural networks could possibly achieve the task of real-time view invariant recognition, which is so ubiquitous in animals (Caley, M.J., 2003) , even those with miniscule nervous systems ...
arXiv:1306.3036v1
fatcat:pkepgbcm35f47gpgzp3hbnly64
A Review on Indian Sign Language Recognition
2013
International Journal of Computer Applications
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Artificial Neural Networks An artificial neural network [16] involves a network of simple processing elements (artificial neurons) which can exhibit complex global behavior, determined by the connections ...
There are several neural networking algorithms which can be used for gesture recognition. The different networks are feed forward networks, Elman neural networks, Self-organizing networks etc. ...
doi:10.5120/13037-0260
fatcat:asgnhpwqwva5tjxhjx5jb23kly
A Neural Network based Real Time Hand Gesture Recognition System
2012
International Journal of Computer Applications
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Finally, a neural network is used to recognize the hand gestures. The performance of the system tested on real data. ...
Based on the experimental results, we noted that this system shows satisfactory performance in hand gesture recognition. General Terms Artificial Neural Network, Image Processing. ...
Klimis Symeonidis developed Hand Gesture Recognition Using Neural Networks [3] [9] paper is a real time vision system for its application within visual interaction environments through hand gesture ...
doi:10.5120/9535-3971
fatcat:bpz4juutcvghbli77stsrukdoe
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